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All Journal Jurnal Teknologi Jurnal Sisfokom (Sistem Informasi dan Komputer) KOMPUTIKA - Jurnal Sistem Komputer Intechno Journal : Information Technology Journal SWAGATI: Journal of Community Service
Robert Marco, Robert
STMIK AMIKOM YOGYAKARTA
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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ANALISIS DAN EVALUASI SISTEM INFORMASI MENGGUNAKAN TECHNOLOGY ACCEPTANCE MODEL (TAM): STUDI KASUS STMIK AMIKOM YOGYAKARTA Marco, Robert
Jurnal Teknologi Vol 9 No 1 (2016): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknologi Industri, Institut Sains & Teknologi AKPRIND Yogyakarta

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Abstract

Pemanfaatan sistem informasi menjadi suatu keharusan yang tidak dapat dihindari oleh setiap Perguruan tinggi yang ingin menempatkan dirinya pada posisi paling depan dalam suatu organisasi pendidikan. Oleh karena itu, kemampuan sistem informasi memegang peranan yang sangat penting untuk menunjang suksesnya sebuah proses operasional seluruh civitas. Penelitian ini, menggunakan metode eksplanatif dengan analisis secara kuantitatif. Model yang digunakan dalam evaluasi sistem informasi, adalah Technology Acceptance Model (TAM). Dengan persamaan satu jalur, dimana variabel bebas terdiri dari kemudahaan (X1) dan manfaat (X2), sedangkan variabel terikat adalah penerima pengguna sistem (Y). Dari hasil yang didapatkan bahwa penggunaan sistem informasi secara jelas berpengaruh positif dan signifikan terhadap kualitas pelayanan yang diberikan oleh mahasiswa, dengan menunjukan bahwa nilai t hitung sebesar 1.034 dan dengan menggunakan tingkata signifikan sebesar 5% (0.05) dengan nilai hitung signifikan sebesar 0.01 sehingga nilai 0.01<0.05 maka data uji t tersebut dianggap singnifikan. Maka dapat disimpulkan bahwa kemanfaatan/penggunaan dan kemudahan sistem informasi mempunyai pengaruh positif dan signifikan terhadap penerimaan sistem di STMIK AMIKOM Yogyakarta.
EEG Emotion Recognition using Deep Neural Network (DNN) in Virtual Reality Environments Agastya, I Made Artha; Marco, Robert; Handayani, Dini Oktarina Dwi
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1903

Abstract

Purpose: The purpose of this study is to explore the integration of EEG technology with virtual reality (VR) systems to enhance therapeutic interventions, improve cognitive state recognition, and develop personalized immersive experiences. Specifically, it investigates the classification of EEG signals in a VR environment using machine learning models and identifies the most effective methods for individual-level analysis.Methods: The study utilized EEG data collected from 31 participants using the Muse 2016 headset, with electrodes positioned according to the 10-20 international system. EEG signals were analyzed for features such as statistical metrics (mean, median, standard deviation, skewness, and kurtosis) and Hjorth parameters (activity, mobility, complexity). Machine learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), were evaluated for their performance in classifying emotional and cognitive states in a VR environment. Result: The results indicate that the Deep Neural Network (DNN) outperformed SVM and KNN models, achieving the highest average classification accuracy. SVM demonstrated consistent performance, with accuracy values consistently above 0.8 across subjects, while KNN showed greater variability and lower overall performance. DNN's architecture, incorporating two hidden layers with ReLU activation and a softmax output layer, demonstrated superior capability in modeling complex EEG patterns. The findings emphasize the effectiveness of DNN in handling high-dimensional and non-linear data, particularly for multi-class classification tasks.Novelty: This study is novel in its focus on personalized machine learning model performance in a VR-EEG setup. Instead of a one-size-fits-all approach, it emphasizes individualized analysis, identifying the most effective model for each participant.
Workshop Duta Pustaka SMAN 1 SLEMAN Firmansyah, Rokhmatulloh B.; Marco, Robert
SWAGATI : Journal of Community Service Vol. 2 No. 3 (2024): November
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2024v2i3.1700

Abstract

SMAN 1 Sleman tiap tahunnya mengikuti lomba komik digital yang diadakan oleh DIKPORA. Tantangan yang mereka hadapi adalah bagaimana cara menemukan bibit unggul dari sekian banyak siswa-siswi SMA yang nantinya akan diikutsertakan kedalam lomba tersebut. Dalam hal ini, SMAN 1 Sleman, mengadakan program workshop Duta Pustaka, serta mengundang penulis sebagai narasumber dan pelatih, yang diadakan pada tanggal 5 Oktober 2023, Kolaboriasi ini selain merupakan penerapan Tri Dharma, juga bertujuan untuk menambah wawasan dan minat siswa terhadap komik digital. Nantinya diharapkan dari workshop yang telah diadakan, dapat diambil siswa-siswi yang berpotensi untuk diikutkan dalam lomba komik digital di tahun selanjutnya.
The Effect of SMOTE and Optuna Hyperparameter Optimization on TabNet Performance for Heart Disease Classification Wijayanto, Danang; Marco, Robert; Sidauruk, Acihmah; Sulistiyono, Mulia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2348

Abstract

Heart disease is a medical condition affecting the cardiovascular system, disrupting blood circulation and reducing cardiac function efficiency, which can lead to severe health complications. Early diagnosis of heart disease has become increasingly crucial as delayed detection can significantly impact patient outcomes and survival rates. While numerous studies have explored various approaches for heart disease classification, challenges related to data imbalance and improper parameter settings remain persistent issues that affect model performance. This research evaluated the effectiveness of combining TabNet with SMOTE and optuna hyperparameter optimization for heart disease classification. We conducted four experimental scenarios using a heart disease dataset with 303 instances: baseline TabNet, baseline TabNet with SMOTE, TabNet with Optuna, and TabNet with both SMOTE and Optuna. Results demonstrated that applying SMOTE alone to TabNet decreased model performance (accuracy from 85.24% to 77.04%, AUC from 0.89 to 0.83). However, when combining SMOTE with Optuna hyperparameter optimization, we achieved optimal performance with 90.16% accuracy, 93.33% precision, 87.50% recall, 90.32% F1-score, and 0.93 AUC. This represented a significant improvement over other configurations and several previous classification approaches. The integration of SMOTE with Optuna optimization  provided an effective framework for heart disease classification that outperformed traditional methods particularly in discriminative capability as evidenced by the superior AUC score.
Optimasi Prediksi Kelayakan Pinjaman dengan Teknik Resampling dan Algoritma Boosting Putra, Muhammad Ricky Perdana; Juwariyah, Siti; Ridwan, Muhammad; Marco, Robert
Komputika : Jurnal Sistem Komputer Vol. 14 No. 2 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i2.15485

Abstract

Loan eligibility assessment is a crucial element in financial risk mitigation, aiming to minimize potential losses due to bad debts and ensure proper resource distribution. Traditional rule-based approaches have limitations in scalability, risk of subjective bias, and complex data management. The application of Machine Learning (ML) presents a solution with the ability to analyze complex patterns in historical data, although significant challenges such as class imbalance where the number of defaulted borrowers is much smaller than that of current borrowers and missing values ​​in the dataset remain major obstacles. This study evaluates the SMOTE and SMOTE-ENN resampling methods, to address class imbalance, as well as the mean imputation technique to handle missing values. By evaluating boosting algorithms, including Gradient Boosting, XGBoost, LightGBM, AdaBoost, and CatBoost, the results show that the combination of the CatBoost algorithm with the SMOTE-ENN sampling technique provides the highest prediction accuracy of 91.67%. This finding confirms the significant potential of ML in improving the accuracy, efficiency, and fairness of predictions, while making important contributions to the development of data-driven decision-making systems in the financial sector.
Co-Authors Acihmah Sidauruk Agastya, I Made Artha Danang Wijayanto Dini Oktarina Dwi Handayani Firmansyah, Rokhmatulloh B. Juwariyah, Siti Muhammad Ridwan Mulia Sulistiyono Putra, Muhammad Ricky Perdana